Template‐based automatic breast segmentation on MRI by excluding the chest region. Issue 12 (13th November 2013)

• Record Type: Journal Article
• Authors: Lin, Muqing; Chen, Jeon‐Hor; Wang, Xiaoyong; Chan, Siwa; Chen, Siping; Su, Min‐Ying
• Is Part Of: Medical physics. Volume 40:Issue 12(2013)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2013-11-13
• Subjects: Magnetic resonance imaging -- Registration -- Segmentation; biomedical MRI -- error analysis -- image registration -- image segmentation -- medical image processing -- muscle -- volume measurement; model‐based breast segmentation -- chest body template -- breast MRI -- breast density -- demons algorithm; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging -- Measuring length, thickness or similar linear dimensions; Measuring angles; Measuring areas; Measuring irregularities of surfaces or contours -- Measuring volume, volume flow, mass flow, or liquid level; Metering by volume -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; Muscles -- Medical imaging -- Radiologists -- Medical image segmentation -- Lungs -- Magnetic resonance imaging -- Medical magnetic resonance imaging -- Cluster analysis -- Medical image contrast -- Edge detection; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4828837
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: : Methods for quantification of breast density on MRI using semiautomatic approaches are commonly used. In this study, the authors report on a fully automatic chest template‐based method. Methods: : Nonfat‐suppressed breast MR images from 31 healthy women were analyzed. Among them, one case was randomly selected and used as the template, and the remaining 30 cases were used for testing. Unlike most model‐based breast segmentation methods that use the breast region as the template, the chest body region on a middle slice was used as the template. Within the chest template, three body landmarks (thoracic spine and bilateral boundary of the pectoral muscle) were identified for performing the initial V‐shape cut to determine the posterior lateral boundary of the breast. The chest template was mapped to each subjectˈs image space to obtain a subject‐specific chest model for exclusion. On the remaining image, the chest wall muscle was identified and excluded to obtain clean breast segmentation. The chest and muscle boundaries determined on the middle slice were used as the reference for the segmentation of adjacent slices, and the process continued superiorly and inferiorly until all 3D slices were segmented. The segmentation results were evaluated by an experienced radiologist to mark voxels that were wrongly included or excluded for error analysis. Results: : The breast volumes measured by the proposed algorithm were very close to the radiologistˈs corrected volumes, showing a % difference ranging from 0.01% to 3.04% in 30 tested subjects with a mean of 0.86% ± 0.72%. The total error was calculated by adding the inclusion and the exclusion errors (so they did not cancel each other out), which ranged from 0.05% to 6.75% with a mean of 3.05% ± 1.93%. The fibroglandular tissue segmented within the breast region determined by the algorithm and the radiologist were also very close, showing a % difference ranging from 0.02% to 2.52% with a mean of 1.03% ± 1.03%. The total error by adding the inclusion and exclusion errors ranged from 0.16% to 11.8%, with a mean of 2.89% ± 2.55%. Conclusions: : The automatic chest template‐based breast MRI segmentation method worked well for cases with different body and breast shapes and different density patterns. Compared to the radiologist‐established truth, the mean difference in segmented breast volume was approximately 1%, and the total error by considering the additive inclusion and exclusion errors was approximately 3%. This method may provide a reliable tool for MRI‐based segmentation of breast density.